Researchers have developed advanced prediction models to identify patients at high risk of readmission 30 days after percutaneous coronary intervention (PCI), enabling clinicians to potentially target them for prevention. The results were published online July 2, 2013, ahead of print in Circulation: Cardiovascular Quality and Outcomes.

Robert W. Yeh, MD, MSc, of Massachusetts General Hospital (Boston, MA), and colleagues looked at all readmissions within 30 days of discharge after PCI in nonfederal hospitals in Massachusetts between October 1, 2005, and September 30, 2008.

Two separate prediction models were developed: a pre-PCI model to predict risk of readmission at the moment of initial presentation and a discharge model to predict risk of readmission at the moment of discharge.

Readmission and Patient Characteristics

Of 36,060 PCI patients surviving to discharge, 10.4% (n = 3,760) were readmitted within 30 days. In bivariate comparisons, readmitted patients were more likely to be:

Elderly (68.1 vs. 64.3 years; P < 0.001)

Female (38.3% vs. 29.6%; P < 0.001)

African-American (4.0% vs. 2.4%; P < 0.001)

In addition, patients with Medicare or state-financed insurance were more likely than those with private insurance or HMO-based insurance to be readmitted (P < 0.001).

Several clinical and angiographic characteristics were more common among patients more likely to be readmitted (P < 0.001 for all comparisons). These included:

In addition, beta-blocker prescription at discharge was associated with a reduced risk of readmission (OR 0.81; 95% CI 0.67-0.96).

Risk Score Based on Pre-PCI Model

After multivariable adjustment, 10 variables were retained in the pre-PCI model: age, sex, insurance type, glomerular filtration rate (GFR) category, CHF (both current and previous), chronic lung disease, peripheral vascular disease, cardiogenic shock at presentation, admission status, and history of previous CABG. Because the pre-PCI model had similar discrimination to the discharge model and could be used early in the course of a patient’s admission, a risk score was created based on the pre-PCI model alone:

Female sex: 2 points

Previous CABG: 1 point

Current CHF: 2 points

Chronic lung disease: 2 points

PAD: 1 point

Cardiogenic shock: 2 points

Age

Under 50 years: 0 points

50 years or more: -1 point

GFR, mL/min

Less than 30: 4 points

30-60: 1 point

Over 60: 0 points

Admission status

Transfer from acute care facility: 3 points

Transfer from nursing home: 4 points

Emergency department: 4 points

Insurance status

Medicare/state: 3 points

Unknown: 4 points

A score of less than 6 denotes low risk, 6 to 10 equals intermediate risk, and ≥ 11 signifies high risk.

The risk score was able to discriminate among patients at low (< 9%), intermediate (10%-21%) and high risk (> 24%) of 30-day readmission after PCI. In a validation cohort from the study, the risk score pinpointed the 6.7% of low-risk patients, 15.9% of intermediate-risk patients, and 26.5% of high-risk patients who were actually readmitted. A Web-based calculator has since been made available at the Mass-DAC Web site for clinicians wishing to use this model.

“To our knowledge, our risk score is the first for 30-day readmission after PCI and may be useful to clinicians, hospital administrators, or investigators designing interventions to reduce readmission after PCI,” the authors state.

Beta-Blockers May Prevent Readmissions

In particular, they note, identifying a patient at high risk for readmission “may prompt more intensive case management attention or in some cases might influence the decision to perform PCI.”

One specific way to reduce readmissions in high-risk patients may be prescription of a beta-blocker, Dr. Yeh and colleagues observe. “Considering a large proportion of readmissions after PCI are related to chest pain, [beta-blockade] may directly reduce hospital readmissions by reducing myocardial oxygen demand and subsequent ischemia,” they conclude.

The authors stress that “future research is needed to identify interventions that can cost-effectively mitigate the rate of readmission. These models to identify higher-risk patients provide an important step in designing such strategies.”

This is important, they note, since the Affordable Care Act includes provisions to provide hospitals with incentives to improve the transition of care from inpatient to outpatient settings and to reduce the $26 billion cost of hospital readmissions to Medicare. Roughly 14.6% of Medicare patients are readmitted to the hospital after PCI, with readmission rates varying substantially between hospitals, according to the authors.

It’s the Patient, Not the Procedure

In a telephone interview with TCTMD, Véronique L. Roger, MD, MPH, of the Mayo Clinic (Rochester, MN), noted, “The interesting finding here is that procedural factors, or in-hospital factors for that matter, don’t add a whole lot statistically to the prediction of readmission risk.”

What that means, Dr. Roger added, “is that most of the risk prediction action occurs upstream, which is before admission or at the time the patient presents for the procedure.”

She stressed that for such a risk score to be applicable to clinical practice, it must be made available in a computerized format. “I’m not going to sit at the bedside and add this up, but that’s not unique to this tool,” Dr. Roger said.

Moreover, she emphasized, the paper does not address what clinicians should do based on the risk score should someone be determined as being at high risk for readmission. “The next step is to say what kind of intervention can we plan to reduce readmission?” Dr. Roger said. “And let’s randomize patients and [use this risk score] and see if you can reduce the risk.”

Dr. Roger pointed out that there were several factors that the researchers were not able to measure that would be important to include in future trials. These include the effects of:

Staged procedures

Transition from inpatient to outpatient care

Home support systems for patients

“We’ve measured patient factors,” she said. “So let’s do a study that measures system factors.”